Robust Subspace Approaches to Visual Learning and recognition

Abstract

In the real world, visual learning is supposed to be a robust and continuous process. All available visual data is not equally important; in the case of occlusions or other undesirable intrusions in the field of view some visual data can even be misleading. Human visual system treats visual data selectively and builds efficient representations of observed objects and scenes even in non-ideal conditions. Furthermore, these representations can afterwards be updated with newly acquired information, thus adapting to the changing world. In this dissertation we study these premises and propose several methods, which introduce similar principles in the machine visual learning and recognition as well. We approach visual learning by the appearance-based modeling of objects and scenes. Models are built using principal component analysis (PCA), which has several shortcomings with respect to the premises mentioned above. In order to overcome these shortcomings, we propose several extensions of the standard PCA. PCA-based learning is traditionally performed in a batch mode, thus requiring all training images to be given in advance. Since this is not admissible in the framework of continuous learning, we propose an incremental method, which processes images sequentially one by one and updates the representation at each step accordingly. Each image can be discarded immediately after the model is updated, which makes the method perfectly well suited for real on-line scenarios. In addition, in the standard PCA approach all pixels of an image receive equal treatment. Also, all training images have equal influence on the estimation of principal subspace. In this dissertation, we present a generalized PCA approach, which estimates principal axes and principal components considering weighted pixels and images. We further extend this weighted approach into a method for learning from incomplete data, which builds the model of an object even when the part of input data is missing. Images of objects and scenes are not always ideal and as such they may contain various deceptive additions like reflections or occlusions. PCA in its standard form is intrinsically non-robust to such non-gaussian noise. Several methods for robust recognition have already been proposed, however robust learning has been tackled very rarely. In the dissertation we introduce a novel approach to the robust subspace learning. The proposed batch and incremental methods detect inconsistencies in the training images and build the representations from consistent data only. As a result, the obtained models are more robust and efficient enabling more reliable visual learning and recognition even when the learning conditions are not ideal. In the dissertation we derive all the methods mentioned above and present suitable algorithms. We also experimentally evaluate all the proposed algorithms on different image domains and determine the applicability of the methods in different scenarios.